Building ML Regression Models using Scikit-Learn

This course walks through building Machine Learning Regression Models using Scikit-Learn library from Python.


   Development, Data Science and AI ML, Machine Learning

  Language - English

   Published on 04/2021



This course is aimed at students and practitioners of Data Sciences for building Predictive Analytics models for research and commercial purposes.

Machine Learning can be used to solve prediction problems for classification and regression. In this course, we discuss about using Machine Learning for building Regression Models. We will use Python Language.

In Python, we have many options for building Machine Learning solutions like Tensor Flow, Keras, etc. In this project, we use Scikit-Learn.

Scikit-Learn provides a comprehensive array of tools for building regression models (Scikit-Learn also has tools for solving classification problems). The concepts learnt in this project can be extended to build Neural Networks and other types of models using tools like Tensor Flow or Keras, etc using Python or any other language like R.

Before diving into building Regression Models using Scikit-Learn, the course discusses the concepts required to understand the process and mechanism for building such models. As it is easy to understand the concepts working them through Excel, and also it can be experienced visually, we start the course through explanation of the associated concepts using Excel.

This course requires the Learners to have prior knowledge of Computer Software programming, knowledge of programming using Python and also some knowledge of Predictive Analytics.

What Will I Get ?

  • Predictive Analytics
  • Regression
  • Linear Regression
  • Random Forest Algorithm
  • Support Vector Machines (SVM) Algorithm
  • Programming for Regression using Scikit-Learn


  • Should have knowledge of writing Computer Software Programs
  • Should have knowledge of Python Programming
  • Should have good knowledge of Excel
  • Some knowledge of Predictive Analytics will be helpful
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